Method for finding the lateral position of the fovea in an SDOCT image volume

ABSTRACT

Embodiments of the present invention provide methods for finding the lateral position of the fovea in an OCT image volume. In one instance, a cost function is developed whose minimum is located at or near the foveal center. This cost function includes one or more measures of retinal layer thickness and/or measures of distance from blood vessels or a priori locations.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.13/299,962, filed Nov. 18, 2011, which is in turn a continuation of U.S.application Ser. No. 12/415,976 filed Mar. 31, 2009 which in turn claimsthe benefit of the filing date under 35 U.S.C. §119(e) of ProvisionalU.S. Patent Application Ser. No. 61/047,525, filed on Apr. 24, 2008, thedisclosure of which is hereby incorporated by reference in its entirety.

TECHNICAL FIELD

The present invention relates to medical imaging, and in particular tosystems that are adapted to displaying views of Optical CoherenceTomography (“OCT”) data.

BACKGROUND

Optical Coherence Tomography (OCT) is an optical imaging technology forperforming in situ real time high-resolution cross sectional imaging oftissue structures at a resolution of less than 10 microns. In recentyears, it has been demonstrated that Fourier domain OCT (FD-OCT) hassignificant advantages in both speed and signal-to-noise ratio ascompared to time domain OCT (TD-OCT). (Leitgeb, R. A., et al., OpticsExpress 11:889-894; de Boer, J. F. et al., Optics Letters 28: 2067-2069;Choma, M. A., and M. V. Sarunic, Optics Express 11: 2183-2189). Theprimary implementations of FD-OCT employ either a wavelength sweptsource and a single detector (Hitzenberger, C. K., et al. (1998) In-vivointraocular ranging by wavelength tuning interferometry. CoherenceDomain Optical Methods in Biomedical Science and Clinical ApplicationsII, SPIE) or a broadband source and an array spectrometer (Hausler, G.and M. W. Lindner (1998). “Coherence Radar” and “Spectral Radar”—NewTools for Dermatological Diagnosis. Journal of Biomedical Optics 3(1):21-31). In TD-OCT, the optical path length between the sample andreference arms needs to be mechanically scanned.

FD-OCT may be either swept source OCT (SS-OCT) or spectrometer basedspectral domain OCT (SD-OCT). In both SS-OCT and SD-OCT, the opticalpath length difference between the sample and reference arm is notmechanically scanned. Instead, a full axial scan (also called A-scan) isobtained in parallel for all points along the sample axial line within ashort time determined by the wavelength sweep rate of the swept source(in SS-OCT) or the line scan rate of the line scan camera (in SD-OCT).As a result, the speed for each axial scan can be substantiallyincreased as compared to the mechanical scanning speed of TD-OCT.

The location of the fovea is clinically important because it is thelocus of highest visual acuity. Automated analyses of retinal thicknessare ideally centered about the fovea, with peripheral regions ofdiminishing importance further from the fovea.

FD-OCT's improved scanning rates enable the rapid acquisition of datawith minimal or correctable motion artifacts. Recent emergence of SD-OCTwithin the field of ophthalmology increases the information available toautomatically detect foveal location than was present in TD-OCTdatasets.

There is limited previous work in the area of automatic fovealidentification for OCT. Previous work on scanning laser tomography [Liet al.] has looked at retinal topography as one method to determinefoveal location, but this is prone to disruption by pathology. Otherwork on foveal identification from fundus photos [Niemeijer et al.,Narasimha-Iyer et al.] benefits from the presence of the Optic NerveHead as a landmark in the image, which is not generally available in OCTimages of the macula at the present state of the art.

The present invention satisfies the need for improved methods forautomatically processing OCT data and identifying the fovea.

SUMMARY

The present invention is defined by the claims and nothing in thissection should be taken as a limitation on those claims. Advantageously,embodiments of the present invention overcome the lack of foveaidentification processing for OCT data.

The fovea has a number of distinguishing anatomical characteristics thatmay be used to identify it in OCT images. The most evident feature of ahealthy fovea is the indentation of the Inner Limiting Membrane (ILM),although the presence of pathology such as edema or posthyaloid membranetraction may disrupt the normal foveal topography. Additionally, theinner retinal layers are thinner near the fovea, and the inner and outerplexiform layers normally pinch together toward the ILM in this region.The fovea has a higher proportion of cone photoreceptors than thesurrounding retina, and their relatively long outer segments can cause apeak in the highly reflective boundary between the inner and outerphotoreceptor segment layers at the fovea. This anatomy can, of course,be disrupted by a variety of outer-retinal pathologies.

Another distinguishing characteristic is that the vasculature emanatingfrom the Optic Nerve Head (ONH) does not extend into the fovea. This ispresumably because the vessels would obscure the dense population ofphotoreceptors that provide acute central vision.

Our invention applies a cost function to various measurements,attributes of measurements, or other information derived from an OCTvolume scan. Our method allows the incorporation of different types ofinformation into a cost function, from which the fovea is automaticallydetected.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional diagram for developing a cost function.

FIG. 2 is an idealized illustration of the key retinal boundary layersin the region of the fovea.

FIG. 3 is an idealized illustration of the vascular regions in theneighborhood of the fovea and the optic nerve head.

FIG. 4 is an idealized illustration of the layers included in an exampleILM slab, shown from a tomographic view.

FIG. 5 is an idealized illustration of the layers included in analternative example ILM slab, shown from a tomographic view.

FIG. 6 (a) is an en-face image formed from integrating a slab volumetaken below the ILM.

FIG. 6 (b) is an image of an example ILM slab contribution to a costfunction.

FIG. 6 (c) is an image of an example blood vessel component contributionto a cost function.

FIG. 6 (d) is an image of an example automatically detected fovea.

FIG. 7 illustrates one design of an optical coherence tomography system.

DETAILED DESCRIPTION

The image analysis primarily seeks a lateral location characterized by arelatively low reflectivity just below the ILM. Avoiding vascularizedregions and/or biasing the decision toward the scan center aids in theidentification of the fovea. A cost function is defined containing termsfrom separate analyses of sub-ILM reflectivity and vessel and scanlocation, and the fovea is determined as the location of minimum cost.

A simple cost function C(x,y) can be expressed as:C(x,y)=c ₁((R*S)(x,y))+c ₂√{square root over (x ² +y ²)}  (Eq. 1)Where x and y are lateral coordinates, S(x, y) is an en-face image ofthe OCT volume (from a partially-integrated fundus projection, forexample), R(x,y) is a radially symmetric filter such as the top-hatfilter, (R*S) is the convolution of the image with the filter, √{squareroot over (x²+y²)} is the usual distance metric, and c₁ and c₂ areconstants chosen to weight the convolution and distance appropriately.In this case, the cost function chooses the point closest to the originthat has the most radially symmetric neighborhood. Since typically OCTvolumes are acquired with the fovea near the center of the en-faceimage, Equation 1 is generally more viable with the origin of theco-ordinate system located at the center of the en-face image.

Alternatively, the cost functionC(x,y,u,v)=c ₁((R*S)(x,y))+c ₂√{square root over ((x−u)²+(y−v)²)}{squareroot over ((x−u)²+(y−v)²)}  (Eq. 2)may be used. Here the location (u, v) is constrained to vary overlocations expected to be near the center of the fovea. This requiresadditional computational power for evaluating the minimum over a fourdimensional space rather than a two dimensional space, but reduces thedependency on the operator acquiring data in the traditional manner.

In general, a cost function could be any linear or nonlinear combinationof location dependent terms that increases as the likelihood of thelocation being at or near the center of the fovea decreases. Forexample, the distance function could be squared in either Equation 1 or2. Alternatively, instead of using a radial filter on the en-face image,the factor or factors may be extracted from tomographic data in theimage volume. It is known that the distance from the Internal LimitingMembrane (ILM) to the Inner Plexiform Layer (IPL) is normally a minimumin the neighborhood of the center of the fovea, so the cost function mayinclude a measure of the distance from the ILM to the IPL. Because ofthe difficulties involved in segmenting the IPL, it is likely thatanother measure of local depression of the ILM would be used, such as ameasure of local curvature or the deviation from a best fit ellipsoidalsurface. The cost function could also include a measure of the retinalthickness as measured by the distance from ILM to Retinal PigmentEpithelium (RPE) or a measure of the vasculature of the region, sincethis distance also is normally a minimum in the neighborhood of thecenter of the fovea. The cost function may include any combination ofthese or other factors, or any function of factors, so long as theresulting cost function attains its minimum either at or near the centerof the fovea. This cost function architecture combines various types ofinformation contained in the image volume to be extracted from thevolume image and combined to locate the center of the fovea.

FIG. 1 illustrates the typical architecture for determining the centerof the fovea using a cost function methodology. Features 110.1 to 110.Nare extracted from the image volume 100 and converted to measures F₁(x,y) to F_(N)(x, y) which attain their minimum at or near the location (x,y) at the center of the fovea. The feature measures are weighted andcombined 120 to create the cost function, whose minimum is determined130 to find the location of the center of the fovea.

In this case, the cost function may look like:

$\begin{matrix}{{{C\left( {x,y} \right)} = {\sum\limits_{k = 1}^{N}{c_{k}{F_{k}\left( {x,y} \right)}}}}{or}} & \left( {{Eq}.\mspace{14mu} 3} \right) \\{{C\left( {x,y} \right)} = {\prod\limits_{k =}^{N}{F_{k}^{c_{k}}\left( {x,y} \right)}}} & \left( {{Eq}.\mspace{14mu} 4} \right)\end{matrix}$or other combinations of the individual measures which preserve theminimum of the cost function at or near the center of the fovea.

FIG. 2 illustrates the boundary layers at the fovea from a tomographicperspective. This idealized sketch illustrates some important featuresof the retinal layers. This illustration shows the narrowing of the gapbetween the ILM 210 and the IPL 220 in the vicinity of the foveal center200. Similarly, the gap between the ILM 210 and the Outer PlexiformLayer (OPL) 230 narrows in the vicinity of the foveal center, as doesthe gap between the IPL 220 and the OPL 230. The peak 260 in theboundary of the Inner and Outer Segments of photoreceptors (IS/OS) 240is caused by the relatively long cone photoreceptors of the IS/OS in theneighborhood of the foveal center. In a healthy retina, the RPE 250 is arelatively flat boundary layer and this illustration shows that thedistance from the RPE to the ILM or IPL layers achieves a minimum eitherat or near the foveal center, while the distance from the RPE to the OPLachieves a maximum at the foveal center. Any number of thesemeasurements can be used as measures contributing to the cost function.

The weights c_(k) in the cost function may be pre-computed from theory,determined empirically or computed as part of the analysis preceding theformation of the cost function. For example, if two measures F₁ and F₂each attain their minimum at the foveal center, but the average noise ofF₁ is twice that of F₂, then a cost functionC(x,y)=0.5×F ₁(x,y)+F ₂(x,y)  (Eq. 5)theoretically balances the noise from the two measurements.Alternatively, the relative weights of the cost function may bedetermined empirically from data by evaluating the minimum of the costfunction for a plurality of choices of the weights and choosing thoseweights which provide the best estimate of the foveal center. Finally,during data analysis, it might be determined that for this data, oneparticular measure cannot be trusted and the weight applied to thatmeasure can be set to zero so that the untrusted measurement does notinfluence the estimate of the location of the foveal center.Alternatively, that measurement might be included, but only with a verysmall weight, so that its influence in incremental, but not dominating.

It should be noted that the vasculature in the neighborhood of the OpticNerve Head (ONH) does not extend into the fovea. Hence, the blood vesselstructure can also be used as a guide to finding the fovea. One usefulmeasure here is the distance from a location to the nearest bloodvessel. Of course, this measure requires a feature identificationprocess for identifying blood vessels. This measure also achieves alocal maximum (not a minimum) at or near the foveal center for mosteyes. One function that is minimal at or near the foveal center is thereciprocal of this distance.

FIG. 3 is an idealized illustration of the vascular regions in theneighborhood of the fovea 300 and the optic nerve head 310. Bloodvessels 320 do not extend into the region of the fovea and the center ofthe fovea is some distance from the nearest blood vessel.

Determining the foveal center using a cost function approach is distinctfrom approaches that may make preliminary hard decisions and thenarbitrate among those preliminary choices.

FIG. 4 illustrates one measure useful in creating a cost function,either alone or in combination with other feature measures. Theillustration is an idealized tomographic view of the retinal layers.This view emphasizes the variations of the tissue reflectivity betweenlayers and over depth. An en-face image is created by summing theintensity data that represents depth reflectivity. Using a segmentationof the ILM surface as an input, we create a volume (slab) and analyzethe intensity of the image volume at each lateral location. Theintensity is summed in depth starting at the ILM 410 and extendingapproximately a fixed distance 450 below the ILM (in this example thedistance is chosen to be about 100 microns). Since the tissue in theupper regions between layers is generally more reflective than thetissue in the deeper regions and the layers are thinnest near the fovealcenter, this sum will achieve its minimum value at or near the center ofthe fovea. In general, the region between the ILM 410 and the IPL 420 isbrighter than the region between the IPL 420 and the OPL 430, which isin turn brighter than the region from the OPL 430 to the RPE 440.

FIG. 5 illustrates an alternative measure. Again, the illustration is anidealized tomographic view of the retinal layers. In this instance,instead of summing the intensity from the ILM over a fixed depth tocreate the en-face image, the intensity is summed from the ILM 510 tothe midpoint 550 between the ILM and RPE. When surface 550 is betweenthe OPL 530 and the RPE 540, this sum includes the intensity from theILM 510, IPL 520 and OPL 530, and the regions between these layers, aswell as a region below the OPL 530 and above the RPE 540. This summeden-face intensity image directly includes intensity information from thethinned regions near the foveal center as well as including fewer pointsin the sum near the foveal center because of the nature of the fovealpit. The intensity in this depth window corresponds to a partial-volumeprojection of the data [Ref. U.S. Pat. No. 7,301,644, Knighton, et al.],but other measures of reflectivity may also be used to similar effect.

The reflectivity analysis (sometimes called an en-face image) of thevolume of image data (sometimes called a slab) just below the ILMachieves a minimum (a dark spot) at the location of a normal fovea. Thisoccurs because the inner retina thins in the vicinity of the fovea, andthe ILM also dips down at this point, so that some of the darker layersof the retina appear in the band a fixed distance below the ILM.Although pathology or imaging artifacts may disrupt the dark spot orcause the appearance of other dark spots in the sub ILM slab intensity,a robust method for detection of this dark spot can locate the foveawell, especially when aided by other information such as vasculature,scan location, or the circularity of the dark spots.

Radially symmetric linear or nonlinear filters such as the Laplacian,Mexican-hat, or top-hat filters are ideal for this application. Sincethe diameter of the fovea is about 1 mm, the lateral window of theanalysis of reflectivity is ideally somewhat less than 1 mm, but shouldbe large enough to exclude artifacts. A top-hat linear filter with 375microns inner radius and 750 microns outer radius gave good results.

A second term in the cost function seeks to locate the foveal avascularzone. Vessel-enhanced images [Frangi et al.; Sato et al.] may be formedfrom partial-volume projections of the OCT data. The vessel image can besmoothed to create an additional term for the cost function. A separateembodiment thresholds and dilates the image to create a mask thatnarrows down the possible locations of the fovea. A distancetransformation of the vessel image has previously been used inestimating fovea locations in scanning laser tomography images. [Li etal.]

For example, FIG. 6 shows (a) an ILM slab, (b) the ILM slab reflectivitycomponent of the cost function, (c) the blood vessel component of thecost function, and (d) is an ILM slab image including indicia (in thiscase, a small circle) overlaid over the image, displaying the computedlocation of the fovea. The location of the fovea was automaticallydetermined in this example using a cost function composed of termsrepresenting the slab reflectivity and the locations of blood vesselswithin the eye.

A term related to distance (only minimal for maximum distances) may beweakly used in the cost function such as the lateral distance from thescan center. This relies on the assumption that the fovea is locatednear the center of the scan, which is generally true. The typicalprotocol for data collection is designed so that the fovea is within theimage cube and substantially separate from the volume boundaries.

Other terms may be included in the cost function, using information suchas the depth of the ILM surface or the thickness of the outer segment ofthe photoreceptors.

The cost function may be modified based on an assessment of the qualityof its inputs. If the sub-ILM slab appears to be free from artifacts,the supplementary information from the vessel-enhanced image may bede-emphasized or excluded entirely. Also, if the quality of thevessel-enhanced image is deemed to be poor, the method may substitutethe distance from the scan center as a supplementary term.

In some cases, the cost function may be used in conjunction withreasoning to make the solution more robust. It is well known thatmeasurement noise is inherent in every measuring technique. Speckle canbe viewed as noise in OCT measurements, since it generally contains nouseful information. Speckle is an image artifact that is due tosubresolution scatterers. Either because of the speckle itself orbecause of the analytic techniques applied to the data to minimize theeffects of speckle, the location of the absolute minimum of the costfunction may not be the center of the fovea. Hence, it is useful toaugment the cost function with additional reasoning, such as a binaryclassifier or a machine learning technique to add robustness to thesolution.

For example, we can perform a histogram of the cost function results andfix a threshold that includes only a small percentage of the data (say1%). Rather than only choosing the minimum as the solution, we selectall points within the threshold of the minimum as potential solutions.We analyze the potential solutions to select the foveal center. We mapthe potential solutions and then process the map using various imageanalysis tools. Since the entire region about the foveal center shouldbe nearly minimal in the cost function, there should be a region aboutthe foveal center in the mapping of the thresholded data. We may erodethis map to remove isolated pixels. We connect neighboring regions usingthe morphological operations of dilation and erosion. The foveal centerwill be a fairly circular elliptical region some distance from any edgeof the image. (The foveal pit is nearly circular and, for the typicalexam, should be well within the image boundaries.) We can apply anothercost function to the connected regions to select the most ellipticalregion that is furthest from the image boundary.

The threshold need not be chosen to be 1% of the data. 0.5% or 2% may beused, or in cases where image levels are well calibrated, a fixedthreshold may be used. Of fundamental importance is that a significantmajority of the data points are eliminated as potential locations of thefoveal center.

Once the fovea is found, its location can be used for a variety ofapplications. The location can simply be displayed for the user to shownas shown in FIG. 6( d) or stored for future use. The location can alsobe temporarily or fleetingly stored and used in the computation ofanother value or parameter, such as the measurement of the macularthickness. The location of the fovea is used to center the bulls-eyefields of the nine subfield grid (similar to the macular grid of theEarly Treatment Diabetic Retinopathy Study). The central subfield(center disc of the bulls-eye) is properly centered on the fovealcenter. A common measure of retinal thickness is the central subfieldthickness. However, locating the fovea also provides the location of theInner Ring Macular Thickness and Outer Ring Macular Thickness.Measurements of the Inner and Outer rings, as well as their Superior,Inferior, Temporal and Nasal subfields, are also used for evaluatingretinal health.

In addition, the location of the fovea can be used as an input for asegmentation algorithm. A layer such as the IS/OS which is generallydifficult to segment can be more easily segmented when even anapproximate location of the fovea is known. Alternatively, iterativetechniques may use an approximate segmentation of a layer to get aninitial estimate of the location of the fovea, which in turn is used toget a better segmentation of the layer, which can then be used to get abetter estimate of the location of the fovea.

Alternatively, the location of the fovea can be used as an input for aregistration algorithm. Disease progression measurement is dependent onrepeated measurements performed at the same location at different times.The location of the fovea can be used as a fixed point to register twoimages of the same eye taken at different times or using differentmodalities. When comparing images taken at different times, the foveallocation can be stored with the original image, stored in a locationassociated with the original image, stored in a patient report, orotherwise stored so as to be available for use on the return visit. Suchstorage may be fleeting, as in local memory storage for use in furtheranalysis. Alternatively, the foveal location can be recomputed from thevolume data at the time that it is needed.

In conjunction with the location of the optic disc, the location of thefovea can be used to determine how the anatomy of a specific eye affectsthe correlation between measured structure and measures of visual fieldfunction. That is, these two locations are key in locating the actualpath of the nerve fibers within the eye. Since the nerve fiber pathwaysare slightly different in each eye, the measurement of the locations ofthe optic disc and the fovea can be instrumental in mapping these fiberpathways and correlating structure to function.

FIG. 7 illustrates an OCT device which can be used to implement thesubject invention. Further information about this type of OCT device isdisclosed in U.S. Patent Publication No. 2007/0291277, incorporatedherein by reference. A low coherence light source 700, typically asuperluminescent diode (SLD), is coupled to source fiber 705 that routeslight to directional coupler 710. The optimal directional strength ofthe coupling depends on system design choices and may be 90/10 (as shownin FIG. 7) or 70/30 or other choices depending on SLD back reflectiontolerance, the source illumination required to image the sample andother system design parameters. Directional coupler 710 splits the lightinto sample fiber 715 and reference fiber 735. The sample path mayinclude a delay apparatus (not shown) to adjust the length of the samplepath. The transverse scanner 720 deflects the OCT beam and preferablycreates a focus in the beam near the region of interest in sample 730.The transverse scanner laterally scans the optical beam across thesample in order to image a volume of the sample.

Some light scattered from sample 730 returns through the scanner anddelay apparatus to sample fiber 715. Coupler 710 routes this lightthrough loop 760 to fiber coupler 750, where it interferes with thereference light. The combining coupler 750 provides two outputs. Theseoutputs can be used for balanced detection (see U.S. Pat. No. 5,321,501FIG. 10). Alternatively, the coupling ratio of coupler 750 can beadjusted to send most of the interfered light to a single OCT detector770. Each OCT detector can be a single photodetector for use intime-domain OCT or swept-source OCT, or a spectrometer for use inspectral domain OCT.

Optional tap 740 diverts a fraction of the reference light to detector745, which may be used to monitor the source power. Monitoring may beincluded to monitor the safety of the sample or to detect a degradationin the source 700. Alternatively, monitoring may not be included at allin the system. The tap removes some fraction of optical power from thereference fiber 735, reducing the power that reaches coupler 750.Sensitivity in OCT can reach the shot-noise limit if the reference poweris large enough to bring the interference signal above receiver noise,but not so large as to bring intensity noise or beat noise above thelevel of shot noise.

The coupling ratios in directional couplers 710, 740 and 750 are chosento set a safe level of illumination to the sample, and to set theappropriate reference power at the detector or detectors. For example,in the case of ophthalmic OCT of the retina using light with wavelengthsnear 850 nm, the safe exposure level is approximately 0.5 mW, and theoptimum reference level at the detector is approximately 0.005 mW.Sources are available in this wavelength range having output power ofapproximately 5 mW. For these conditions one would use a coupling rationear 90%/10% in the splitting coupler 710 so that 10% of the sourcepower reaches the sample. 90% of the scattered light will then be routedto loop 760. In the case where there is a single OCT detector 770, thecombining coupler 750 preferably routes most of the sample light to thatdetector. The splitting coupler routes 90% of source light, 4.5 mW, toreference fiber 735, while only 0.005 mW is required at the detector.One could use a combining coupler 750 that couples 0.1% of the referencelight into the single OCT detector 770, but in manufacture it isdifficult to control the 0.1% coupling factor. A preferred solution isto use a 99%/1% split ratio in combining coupler 750, and take advantageof the additional degree of freedom in tap 740 to adjust the referencepower. Nominally, tapping 89% of the power form reference fiber 735 willprovide an appropriate reference level of 0.005 mW at OCT detector 770,in this example.

As an alternative to adjusting the tap ratio of optional tap 740, onecan adjust the reference level by including attenuating fiber (U.S. Pat.No. 5,633,974) in the reference path.

The output of the detector 770 is routed to processor 780. Thisprocessor may be a single device or a plurality of devices,preferentially optimized for their portion of processing. The processor780 is connected to one or more peripherals providing a user interfacedevices, such as display 790. Display 790 can be used to generate imagesof the type shown in FIG. 6 d. The processor might also be connected toother user interface devices (such as a keyboard, mouse, joystick, andothers), and one or more external communication devices (such as a USBor network connector, optical storage media, printer, internet, andothers), as well as possibly connecting to other imaging hardware (suchas cameras, fixation targets, fundus viewers, and others) or peripheralpatient devices (such as head support, height adjustment, and others)which are not shown. The processor 780 provides the computational power(in one or more modules) processing functions such as image formation,volume rendering, segmentation, registration, evaluation of costfunctions, and/or other computational tasks required for medical imagingand analysis.

It should be understood that the embodiments, examples and descriptionshave been chosen and described in order to illustrate the principles ofthe invention and its practical applications and not as a definition ofthe invention. Modifications and variations of the invention will beapparent to those skilled in the art. For example, while SD-OCT systemsare the most likely implementation, similar performance SS-OCT systemsare expected to achieve similar results and even TD-OCT systems can beused, with degraded performance. The scope of the invention is definedby the claims, which includes known equivalents and unforeseeableequivalents at the time of filing of this application.

The following references are hereby incorporated herein by reference.

US Patent Documents

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What is claimed is:
 1. An apparatus for generating 3D image informationfrom within the eye of a patient and for identifying the location of thefovea from within the image information, said apparatus comprising: anoptical coherence tomography (OCT) system, said system including a lightsource, a beam splitter for dividing the light along a sample arm and areference arm, said sample arm further including a scanner for scanningthe beam transversely over the eye, said OCT device including a detectorfor receiving light returned from both the sample and the reference armsand generating signals corresponding to three dimensional imageinformation based on a reflection distribution as a function of deptharising from reflecting features in the eye; and a processor forassigning a single representative intensity value for each transverselocation across a transverse image slab within the three dimensionalimage information and determining the location of the fovea based on thetransverse location having a minimum representative intensity value. 2.An apparatus are recited in claim 1, further including a display fordisplaying the image information and the determined foveal location. 3.An apparatus as recited in claim 1, wherein the representative intensityvalue corresponds to a sum of the intensities as a function of depthacross the transverse image slab.
 4. An apparatus as recited in claim 1,wherein the representative intensity value corresponds to an integratedintensity as a function of depth across the transverse image slab.
 5. Anapparatus as recited in claim 1, wherein the processor segments thetransverse image slab to define a region beginning at the inner limitingmembrane (ILM) and extending to a predetermined depth below the ILM andwherein the processor assigns the single representative intensity valuesto the transverse positions within the segmented region.
 6. An apparatusas recited in claim 1, wherein the processor evaluates a second,different foveal location dependent image quantity that reaches anextremum as the likelihood of that location being at or near the centerof the fovea increases and wherein the results of the evaluation of thesecond different foveal location dependent image quantity are used toimprove the determination of the location of the fovea.
 7. An apparatusas recited in claim 1, wherein said processor determines the location ofthe blood vessels in the image information and utilizes the blood vessellocation information to improve the determination of the location of thefovea.
 8. An apparatus as recited in claim 1, wherein when determiningthe location of the fovea, the processor further considers the lateraldistance from the center of the scanned region in the plane of the scan.9. An apparatus as recited in claim 1, wherein said processor determinesthe thickness of the macular layer in the region of the determinedfoveal location.
 10. An apparatus as recited in claim 1, wherein theprocessor segments the transverse image slab to define a regionbeginning at the inner limiting membrane (ILM) and extending to a depthbelow the ILM, said depth being determined as a function of the distancefrom the ILM to the retinal pigment epithelium (RPE) and wherein theprocessor assigns the single representative intensity values to thetransverse positions within the segmented region.
 11. An apparatus asrecited in claim 1, wherein the processor maps the nerve fiber pathwaysusing the determined foveal location and the location of the optic disc.12. An apparatus for generating 3D image information from within the eyeof a patient and for identifying the location of the fovea from withinthe image information, said apparatus comprising: an optical coherencetomography (OCT) system, said system including a light source, a beamsplitter for dividing the light along a sample arm and a reference arm,said sample arm further including a scanner for scanning the beamtransversely over the eye, said OCT device including a detector forreceiving light returned from both the sample and the reference arms andgenerating signals corresponding to three dimensional image informationbased on a reflection distribution as a function of depth arising fromreflecting features in the eye; and a processor analyzing the imageinformation to determine the location of the fovea within the image,said analyzing step including evaluating a quantity associated with eachof at least two different independent indicators of foveal location,said two different indicators being combined and weighted, wherein oneof said indicators of foveal location is determined by assigning asingle representative intensity value for each transverse locationacross a transverse image slab within the three dimensional imageinformation and determining the location of the fovea based on thetransverse location having a minimum representative intensity value. 13.An apparatus are recited in claim 12, further including a display fordisplaying the image information and the determined foveal location. 14.An apparatus as recited in claim 12, wherein the representativeintensity value corresponds to a sum of the intensities as a function ofdepth across the transverse image slab.
 15. An apparatus as recited inclaim 12, wherein the representative intensity value corresponds to anintegrated intensity as a function of depth across the transverse imageslab.
 16. An apparatus as recited in claim 15, wherein the integratedintensity is evaluated in a region near the inner limiting membrane(ILM).
 17. An apparatus as recited in claim 12, wherein one of saidindicators of foveal location relates to the location of blood vesselsin the image information.
 18. An apparatus as recited in claim 12,wherein the relative weighting of the indicators is adjusted based onthe level of noise associated with the respective measurements.
 19. Anapparatus as recited in claim 12, wherein said processor determines thethickness of the macular layer in the region of the determined foveallocation.
 20. An apparatus for generating 3D image information fromwithin the eye of a patient and for generating a partial en face imagefrom a subset of the 3D image information, said apparatus comprising: anoptical coherence tomography (OCT) system, said system including a lightsource, a beam splitter for dividing the light along a sample arm and areference arm, said sample arm further including a scanner for scanningthe beam transversely over the eye, said OCT device including a detectorfor receiving light returned from both the sample and the reference armsand generating signals corresponding to three dimensional imageinformation based on a reflection distribution as a function of deptharising from reflecting features in the eye; and a processor foridentifying first and second surfaces within the three dimensional imageinformation and defining a segmented region between the first surfaceand a boundary, said boundary being a predetermined fractional distancebetween the first surface and the second surface, said processor forassigning a single representative intensity value for each transverselocation across a transverse image slab within the segmented region andfor generating an en face image using the representative intensityvalues.
 21. An apparatus as recited in claim 20, wherein therepresentative intensity value corresponds to sum of the intensities asa function of depth across the transverse image slab.
 22. An apparatusas recited in claim 20, wherein the representative intensity valuecorresponds to an integrated intensity as a function of depth across thetransverse image slab.
 23. An apparatus as recited in claim 20, whereinthe predetermined fractional distance is the midpoint between the firstsurface and the second surface.
 24. An apparatus as recited in claim 20,further including a display for displaying the generated en face image.